Energy management strategy of hybrid energy storage system for electric vehicles based on genetic algorithm optimization and temperature effect

Energy management strategy plays a decisive role in the energy optimization control of electric vehicles. The traditional rule-based and fuzzy control energy management strategy relies heavily on expert experience. In this paper, a genetic algorithm (GA)-optimized fuzzy control energy management strategy of hybrid energy storage system for electric vehicle is presented. First, a systematic characteristic experiment of lithium-ion batteries and ultracapacitors is performed at different temperatures. Second, the accurate battery and ultracapacitor models are established at different temperatures and the performances are analyzed in details. Next, the GA is used to optimize the formulation of the fuzzy membership function with the minimum energy loss as the objective. Based on the comprehensive discussion, it indicates that the GA-optimized strategy has better performance than that of non-optimization strategy. In addition, to verify the robustness of this method, the experiment data is further validated at different ambient temperatures (10 degrees C, 25 degrees C, 40 degrees C). The results show that the energy economy of electric vehicles increased by 2.6%, 2.4%, and 3.3% at 10 degrees C, 25 degrees C and 40 degrees C, respectively.

Automated summary

In this paper, a genetic algorithm (GA)-optimized fuzzy control energy management strategy for electric vehicles is proposed, which can improve the energy economy of electric vehicles.